The final course explores advanced techniques to enhance our RL systems. We'll implement random goal positions, hazardous environments with mines, and reward shaping, concluding with an exploration of cutting-edge RL developments that point toward future applications.
Overview
Syllabus
- Lesson 1: Implementing Random Goals in Grid World Environments
- Lesson 2: Reward Shaping for Faster Learning in Reinforcement Learning
- Lesson 3: Navigating Environmental Hazards in Reinforcement Learning
- Lesson 4: Designing Effective State Representations in Reinforcement Learning
- Lesson 5: Exploring the Future of Reinforcement Learning
